3D simulations and movement control with PyBullet. This article demonstrates how to build a 3D environment with PyBullet for manually controlling a robotic arm, covering setup, robot loading, movement control (position, velocity, force), and interaction with objects.
An exploration of simple transformer circuit models that illustrate how superposition arises in transformer architectures, introducing toy examples and analyzing their behavior.
The core mechanics of Deep Learning, and how to think the PyTorch way. This guide provides a whirlwind tour of PyTorch’s methodologies and design principles, covering tensors, automatic differentiation, and training custom neural networks.
A unified memory stack that functions as a memristor as well as a ferroelectric capacitor is reported, enabling both energy-efficient inference and learning at the edge.
A comprehensive guide covering the most critical machine learning equations, including probability, linear algebra, optimization, and advanced concepts, with Python implementations.
An Apple study shows that large language models (LLMs) can improve performance by using a checklist-based reinforcement learning scheme, similar to a simple productivity trick of checking one's work.
This article provides a gentle introduction to Q-learning, its principles, and the basic characteristics of its algorithms, presented in a clear and illustrative tone.
The article discusses the evolution of model inference techniques from 2017 to a projected 2025, highlighting the progression from simple frameworks like Flask and FastAPI to more advanced solutions like Triton Inference Server and vLLM. It details the increasing demands on inference infrastructure driven by larger and more complex models, and the need for optimization in areas like throughput, latency, and cost.
DeepMind introduces Ithaca, a deep neural network that can restore damaged ancient Greek inscriptions, identify their original location, and help establish their creation date, collaborating with historians to advance understanding of ancient history.
This blog post details the training of 'Chess Llama', a small Llama model designed to play chess. It covers the inspiration behind the project (Chess GPT), the dataset used (Lichess Elite database), the training process using Huggingface Transformers, and the model's performance (Elo rating of 1350-1400). It also includes links to try the model and view the source code.
